Prediction of Customer Interest in E-Commerce Data using Sentimental Analysis

Online retailers manage enormous customer datasets containing review feedback as well as ratings information. Expansion of business sales together with superior customer experiences requires businesses to comprehend customer interests and forecast upcoming purchasing patterns. The current evaluation methods utilize both heuristic models through rule-based systems while demonstrating restricted capabilities for expansion and flexibility. Modern developments in deep learning together with sentiment analysis technologies produce better predictions of customer interest through the analysis of product review text alongside social media content. This research recommends a sophisticated sentiment analysis solution that implements Bidirectional Encoder Representation from Transformers (BERT) to advance sentiment detection along with customer interest forecasting. The proposed solution exceeds traditional techniques because it uses contextual word representations together with transformer self-attention processing to identify subtle semantic patterns in written text.